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69<h1>kalman.h File Reference</h1>
70<p>Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions. 
71<a href="#_details">More...</a></p>
72<code>#include &quot;../math/functions.h&quot;</code><br/>
73<code>#include &quot;../stat/exp_family.h&quot;</code><br/>
74<code>#include &quot;../math/chmat.h&quot;</code><br/>
75<code>#include &quot;../base/user_info.h&quot;</code><br/>
76
77<p><a href="kalman_8h_source.html">Go to the source code of this file.</a></p>
78<table border="0" cellpadding="0" cellspacing="0">
79<tr><td colspan="2"><h2>Classes</h2></td></tr>
80<tr><td class="memItemLeft" align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classbdm_1_1StateSpace.html">bdm::StateSpace&lt; sq_T &gt;</a></td></tr>
81<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Basic elements of linear state-space model.  <a href="classbdm_1_1StateSpace.html#_details">More...</a><br/></td></tr>
82<tr><td class="memItemLeft" align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classbdm_1_1Kalman.html">bdm::Kalman&lt; sq_T &gt;</a></td></tr>
83<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Common abstract base for <a class="el" href="classbdm_1_1Kalman.html" title="Common abstract base for Kalman filters.">Kalman</a> filters.  <a href="classbdm_1_1Kalman.html#_details">More...</a><br/></td></tr>
84<tr><td class="memItemLeft" align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classbdm_1_1KalmanFull.html">bdm::KalmanFull</a></td></tr>
85<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Basic <a class="el" href="classbdm_1_1Kalman.html" title="Common abstract base for Kalman filters.">Kalman</a> filter with full matrices.  <a href="classbdm_1_1KalmanFull.html#_details">More...</a><br/></td></tr>
86<tr><td class="memItemLeft" align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classbdm_1_1KalmanCh.html">bdm::KalmanCh</a></td></tr>
87<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight"><a class="el" href="classbdm_1_1Kalman.html" title="Common abstract base for Kalman filters.">Kalman</a> filter in square <a class="el" href="classbdm_1_1root.html" title="Root class of BDM objects.">root</a> form.  <a href="classbdm_1_1KalmanCh.html#_details">More...</a><br/></td></tr>
88<tr><td class="memItemLeft" align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classbdm_1_1EKFfull.html">bdm::EKFfull</a></td></tr>
89<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Extended <a class="el" href="classbdm_1_1Kalman.html" title="Common abstract base for Kalman filters.">Kalman</a> Filter in full matrices.  <a href="classbdm_1_1EKFfull.html#_details">More...</a><br/></td></tr>
90<tr><td class="memItemLeft" align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classbdm_1_1EKFCh.html">bdm::EKFCh</a></td></tr>
91<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Extended <a class="el" href="classbdm_1_1Kalman.html" title="Common abstract base for Kalman filters.">Kalman</a> Filter in Square <a class="el" href="classbdm_1_1root.html" title="Root class of BDM objects.">root</a>.  <a href="classbdm_1_1EKFCh.html#_details">More...</a><br/></td></tr>
92<tr><td class="memItemLeft" align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classbdm_1_1MultiModel.html">bdm::MultiModel</a></td></tr>
93<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">(Switching) Multiple Model The model runs several models in parallel and evaluates thier weights (fittness).  <a href="classbdm_1_1MultiModel.html#_details">More...</a><br/></td></tr>
94<tr><td class="memItemLeft" align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classbdm_1_1StateCanonical.html">bdm::StateCanonical</a></td></tr>
95<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">conversion of outer <a class="el" href="classbdm_1_1ARX.html" title="Linear Autoregressive model with Gaussian noise.">ARX</a> model (<a class="el" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>) to state space model  <a href="classbdm_1_1StateCanonical.html#_details">More...</a><br/></td></tr>
96<tr><td colspan="2"><h2>Functions</h2></td></tr>
97<tr><td class="memItemLeft" align="right" valign="top"><a class="anchor" id="a5e1b3b4da814283b69eed3dfed7636ce"></a><!-- doxytag: member="kalman.h::UIREGISTER" ref="a5e1b3b4da814283b69eed3dfed7636ce" args="(KalmanFull)" -->
98&nbsp;</td><td class="memItemRight" valign="bottom"><b>bdm::UIREGISTER</b> (KalmanFull)</td></tr>
99<tr><td class="memItemLeft" align="right" valign="top"><a class="anchor" id="a79179c84c0f2663fb533255fa90bb5e6"></a><!-- doxytag: member="kalman.h::UIREGISTER" ref="a79179c84c0f2663fb533255fa90bb5e6" args="(KalmanCh)" -->
100&nbsp;</td><td class="memItemRight" valign="bottom"><b>bdm::UIREGISTER</b> (KalmanCh)</td></tr>
101<tr><td class="memItemLeft" align="right" valign="top"><a class="anchor" id="afd9a545e845b00256e0bdfc01c4f811c"></a><!-- doxytag: member="kalman.h::UIREGISTER" ref="afd9a545e845b00256e0bdfc01c4f811c" args="(EKFfull)" -->
102&nbsp;</td><td class="memItemRight" valign="bottom"><b>bdm::UIREGISTER</b> (EKFfull)</td></tr>
103<tr><td class="memItemLeft" align="right" valign="top"><a class="anchor" id="aea9e7d50fdc217be77cd6b964ff0f1c6"></a><!-- doxytag: member="kalman.h::UIREGISTER" ref="aea9e7d50fdc217be77cd6b964ff0f1c6" args="(EKFCh)" -->
104&nbsp;</td><td class="memItemRight" valign="bottom"><b>bdm::UIREGISTER</b> (EKFCh)</td></tr>
105<tr><td class="memItemLeft" align="right" valign="top"><a class="anchor" id="aab7fcf226fa0a65350b0f3d4624963f9"></a><!-- doxytag: member="kalman.h::SHAREDPTR" ref="aab7fcf226fa0a65350b0f3d4624963f9" args="(EKFCh)" -->
106&nbsp;</td><td class="memItemRight" valign="bottom"><b>bdm::SHAREDPTR</b> (EKFCh)</td></tr>
107<tr><td class="memItemLeft" align="right" valign="top"><a class="anchor" id="a84ed423eb4acd7f88d162a6f38a52c52"></a><!-- doxytag: member="kalman.h::UIREGISTER" ref="a84ed423eb4acd7f88d162a6f38a52c52" args="(MultiModel)" -->
108&nbsp;</td><td class="memItemRight" valign="bottom"><b>bdm::UIREGISTER</b> (MultiModel)</td></tr>
109<tr><td class="memItemLeft" align="right" valign="top"><a class="anchor" id="ae2745b9ba389d8d4ccc779e76f921a49"></a><!-- doxytag: member="kalman.h::SHAREDPTR" ref="ae2745b9ba389d8d4ccc779e76f921a49" args="(MultiModel)" -->
110&nbsp;</td><td class="memItemRight" valign="bottom"><b>bdm::SHAREDPTR</b> (MultiModel)</td></tr>
111</table>
112<hr/><a name="_details"></a><h2>Detailed Description</h2>
113<p>Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions. </p>
114<dl class="author"><dt><b>Author:</b></dt><dd>Vaclav Smidl.</dd></dl>
115<p>----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty</p>
116<p>Using IT++ for numerical operations ----------------------------------- </p>
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